The Influence of Emotional States on Short-term Memory Retention

by using Electroencephalography (EEG) Measurements: A Case

Study

Ioana A. Badara

, Shobhitha Sarab

, Abhilash Medisetty

, Allen P. Cook

, Joyce Cook

and Buket D. Barkana

School of Education, University of Bridgeport, 221 University Ave., Bridgeport, Connecticut, 06604, U.S.A.

Department of Electrical Engineering, University of Bridgeport, 221 University Ave., Bridgeport, Connecticut, 06604,

U.S.A.

Keywords: Memory, Learning, Emotions, EEG, ERP, Neuroscience, Education.

Abstract: This study explored how emotions can impact short-term memory retention, and thus the process of learning,

by analyzing five mental tasks. EEG measurements were used to explore the effects of three emotional states

(e.g., neutral, positive, and negative states) on memory retention. The ANT Neuro system with 625Hz

sampling frequency was used for EEG recordings. A public-domain library with emotion-annotated images

was used to evoke the three emotional states in study participants. EEG recordings were performed while each

participant was asked to memorize a list of words and numbers, followed by exposure to images from the

library corresponding to each of the three emotional states, and recall of the words and numbers from the list.

The ASA software and EEGLab were utilized for the analysis of the data in five EEG bands, which were

Alpha, Beta, Delta, Gamma, and Theta. The frequency of recalled event-related words and numbers after

emotion arousal were found to be significantly different when compared to those following exposure to neutral

emotions. The highest average energy for all tasks was observed in the Delta activity. Alpha, Beta, and

Gamma activities were found to be slightly higher during the recall after positive emotion arousal.

1 INTRODUCTION

Neuroscientists and educators have long focused on

the role of emotions in the process of learning. This

interest in such relationship became evident in

neuroscience laboratories and classrooms,

nevertheless separate from one another. It was only in

recent years that the two camps united to

acknowledge the fact that emotion guides the learning

process and overall, has a major impact on cognition

(Greene et al., 2001; Goswami, 2006; Immordino-

Yang and Damasio, 2007). It is well known that

positive and negative emotions affect the brain’s

function (LeDoux, 2000; Damasio, 2005). For

instance, positive emotions, such as joy, serenity,

happiness, gratitude, and love, have a positive impact

on the brain’s capacity to learn. These emotions can

cause an increased ability to learn and solve

problems. On the other hand, negative emotions, such

as hate, anger, sadness or fear, can impair the brain’s

capacity to learn. In consequence, educators can use

the neuro-scientific perspective on learning to create

emotional climates in the classroom that can be

conducive to learning. This case study aims to

provide support for such actions by investigating the

impact of emotion on memory retention through the

analysis of five electroencephalography (EEG)

bands.

2 COMPARISON WITH

RELATED WORK

Psychological studies have long shown that subjects’

emotional states have an influence on their learning

and memory. Researchers in psychology have carried

out many experimental techniques in order to find a

relationship between emotion, learning, and memory.

For instance, in an early experiment performed by

Bower and collaborators (1978), study participants

were asked to recall words from two lists, one list

learned while they were happy, and the other learned

A. Badara I., Sarab S., Medisetty A., Cook A., Cook J. and D. Barkana B.

The Inﬂuence of Emotional States on Short-term Memory Retention by using Electroencephalography (EEG) Measurements: A Case Study.

DOI: 10.5220/0006171402050213

In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 205-213

ISBN: 978-989-758-212-7

205

while they were sad. Emotional states (happiness and

sadness) were induced via hypnotic suggestions.

Highly hypnotizable volunteers performed the

experimental task while maintaining their mood

(happy or sad) for 5 to 20 minutes. Later they recalled

both lists when they were in one mood or the other.

People who were found to be in a general sad state

recalled more of the list they had learned while being

sad from hypnosis, and people who were found to be

generally happy recalled more of the list they learned

while being happy from hypnosis. Neuro-scientific

studies have also shown that emotions exert

influences on various behavioral and cognitive

processes such as concentration, long-term memory,

and decision-making (Bower, Monteiro, and Gilligan,

1978; Bower, Gilligan, and Monteiro, 1981).

Emotions cause a change of perception of

environmental stimuli, which in turn become more

significant, lead to deeper neuronal processing, and

affect memory (Clark and Fiske, 1982). Positive

emotions have been proven essential for cognitive

organization, thought processes, creativity, flexibility

in problem solving, and intrinsic motivation (Isen,

2000; Kort, Reilly, and Picard, 2001; Chaffar and

Frasson, 2004). It has also been reported that people

who are anxious have slow decision latency, deficit in

inductive reasoning, and reduced memory capacity

(Bower, Gilligan, and Monteiro, 1981).

Electroencephalography (EEG) measurements, as

well as positron emission tomography (PET) or

functional magnetic resonance imaging (fMRI)

techniques, are noninvasive techniques used to detect

changes in the brain activity. These changes in large-

scale electrical potentials, in the case of EEG, or in

regional blood flow in brain regions, in the case of

fMRI, can be correlated with various processes that

take place in the brain. For example, fMRI measures

brain activity by detecting changes associated with

blood flow. This technique relies on the fact that

increased cerebral blood flow and neuronal activation

are coupled.

The electroencephalogram (EEG) is a test that

records the electrical activity of the brain as wave

patterns generated by various brain structures. The

electrical activity of an alternating type is recorded

from the scalp surface after being picked up by metal

electrodes (small metal discs) and conductive media.

EEG reading is a completely non-invasive procedure

that can be applied repeatedly to normal adults and

children with virtually no risk or limitation. EEG

measurements are frequently used in medicine for the

detection of problems associated with certain brain

disorders (i.e., seizure disorders, stroke, brain tumors,

dementia, etc.). Research and clinical applications of

the EEG in humans and animals are used to monitor

alertness, coma and brain death; locate areas of

damage following head injury, stroke, tumor, etc.; test

afferent pathways (by evoked potentials); monitor

cognitive engagement (alpha rhythm); produce

biofeedback situations, alpha, etc.; control anesthesia

depth (“servo anesthesia”); investigate epilepsy and

locate seizure origin; test epilepsy drug effects; assist

in experimental cortical excision of epileptic focus;

monitor human and animal brain development; test

drugs for convulsive effects; investigate sleep

disorder and physiology; human-computer

interaction, and much more (Bickford, 1987). No

studies to date have been reported in the literature to

use EEG data in order to investigate the impact of

emotional states on memory, thus on the process of

learning.

Computer applications that can detect the

emotional state of the user are still in demand in an

effort to copy human communication. Facial

expressions and voice signals have been heavily

studied to detect emotions, especially in the last two

decades (Fox et al., 2000; Bartlett, Littlewort, Fasel,

and Movellan, 2003; Hudlicka, 2003). Emotion

classification accuracies lie between 80-90% in such

studies. From a physiological standpoint, there is a

net separation between physiological arousal,

behavioral expression, and the conscious experience

of an emotion. Additionally, physiological studies

indicate that face and voice can represent emotional

states which can be adapted and therefore, their

interpretation is not objective. For example, one can

smile while feeling sad. In this case, emotion

recognition systems by using facial expression will

not be able to detect the true emotion.

In contrast, EEG measurements have been shown

to correlate with emotional states (Sammler,

Grigutsch, Fritz, and Koelsch, 2007; Mauss and

Robinson, 2009). Currently, the sensitivity of EEG-

based recognition of artificially evoked emotion has

been reported around 60% (Bos, 2006). Although

there are many studies in the field of EEG-based

emotion recognition, there is much to be done to

understand and improve current EEG-based emotion

recognition systems. In addition, no studies have been

carried out to directly investigate the effects of

emotion on memory retention, and thus the learning

process, by using EEG measurements. Some of the

studies indirectly related to this research are

mentioned below.

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206

A review of the research literature performed by

Banich and colleagues (2009) revealed that working

memory and long-term memory can influence, and in

turn are influenced by, emotional processes. They

concluded that lateral prefrontal regions and the

anterior cingulate cortex are recognized as playing a

large role in the cognitive control and emotional

information in both working memory and long-term

memory. EEG data was not utilized in their work.

Nonetheless, Klimesch (1999) presented evidence

that EEG oscillations in the alpha and theta bands

reflected cognitive and memory performance, which

were related to two types of EEG phenomena: a tonic

increase in alpha, but a decrease in theta power, and a

large event-related decrease in alpha but increase in

theta, depending on the type of memory demands.

Additionally, Berka and collaborators (2007) studied

task engagement and mental workload in vigilance,

learning, and memory tasks using EEG data. They

showed that EEG workload increased with increased

working memory load and during problem solving,

integration of information, and analytical reasoning,

leading to the fact that EEG engagement reflected

information-gathering, visual processing, and

allocation of attention.

In order to get a better understanding of the effects

of various emotions on our capacity to memorize and

learn, this work employed EEG measurements to

investigate the relationship between emotion and

memory. More specifically, our study compared

event-related potentials (ERP) measured by EEG

when triggered by emotions (sadness, happiness,

neutral emotion) and engagement in several tasks

(memorization of words and numbers). The outcome

of such research in the field of education can shed

light on how emotions impact memory retention. In

addition to the education field, this study’s findings

can also provide valuable information to the field of

affective computing, an emerging topic in human-

computer interaction that tries to satisfy the need of

the user.

3 RESEARCH IDEAS AND

RESULTS

This study investigates the influence of emotional

states on memory retention by evaluating the effects

of five mental tasks in EEG bands. The study outline

is depicted in Figure 1. The five mental tasks were: 1)

the baseline task, for which the subjects were asked

to relax as much as possible; 2) the letter task, for

which the subjects were asked to memorize ten

words; 3) the math task, for which the subjects were

asked to memorize ten numbers; 4) the visual task, for

which the subjects were asked to watch a set of

images; and 5) the recall test, for which the subjects

were asked to recall the words and numbers presented

them earlier. The visual task was separated into three

categories corresponding to the type of images

extracted from the IAPS library and the

corresponding emotional state assumed to be evoked

by them: neutral, sadness, and happiness.

Figure 1: The study outline.

Steps 0-1-2: EEG Data Acquisition.

The impact of three emotions (i.e., neutral /calm,

negative/sad, and positive/happy) on memory was

analyzed. Each emotion was evoked by a 1-minute

exposure to a set of twenty images from the

International Affective Picture System (IAPS; Lang,

Bradley, and Cuthbert, 2005). Event-related brain

potentials (ERPs) were recorded throughout the

duration of the test, as explained in Figure 2. An

event-related potential (ERP) represents the electrical

signal detected by EEG and indicates the effect of the

stimulus on the brain, millisecond by millisecond

following the stimulus. ERPs are averaged to

eliminate the background and provide a ‘picture’ of

the brain activity induced by a stimulus. The rate of

these changes may vary from milliseconds to years,

depending on what is being learned. Nonetheless, for

a simple task such as memorizing 10 numbers/words,

it has been shown that ERPs recorded from

milliseconds up to 1 minute, would serve as good

indicators of the brain changes that took place during

the task (Dehaene, 1996). Consequently, this study

analyzed short-term ERPs.

Each participant was exposed to each emotional

stimulus for 1 minute. Before exposure to a stimulus,

a list of words and a list of numbers were presented

back to back for 15 s for memorization. The word lists

contained ten words as five event-related and five not

event-related words. Event-related words include

The Inﬂuence of Emotional States on Short-term Memory Retention by using Electroencephalography (EEG) Measurements: A Case Study

207

Figure 2: EEG data acquisition setup with five mental tasks.

words that directly relate to the type of emotion

triggered. For example, when participants were

exposed to negative visual stimuli, such as images of

mutilated individuals, the event-related words

included ‘horror’. For positive visual stimuli, such as

images of laughing babies, the event-related words

included ‘caring’. Immediately after the exposure to

each set of emotional stimuli, each participant was

asked to recall and write down the words and numbers

from the list.

Participants: Participants were informed in advance

about the procedure and the topic of research. Each

participant signed a consent form after they were

informed. A short test was run for 1 minute during

which the participant was exposed to 3 neutral visual

stimuli in order to prepare the subject for the actual

recording. The goal of the short test was to familiarize

participants with the recording procedure in order to

prevent any anxiety-related EEG measurements. Six

male participants (enrolled in a graduate level

engineering program at the time of the study) were

recruited from the School of Engineering graduate

program. All participants had science and engineering

education backgrounds and belonged to the same age

group (between 20 and 25 years old). The research

was approved by the IRB. The fact that no female

participants took part in the study is considered as

study limitation and is discussed in the conclusion

section. Currently, the study has been extended and is

ongoing in the hope that investigators would be able

to recruit female participants and overall, enlarge the

sample size.

Software/EEG Recording: The ANT Neuro system

with 625Hz sampling frequency was used for EEG

measurements. The ANT WaveGuard cap, non-

magnetic and very comfortable, had 32 Ag/AgCl

sintered electrode configuration. Electrode cap with

electrodes were placed by following the 10-20

electrode placement system (Klem, LuÈders, Jasper,

and Elger, 1999). Contact between the WaveGuard

electrodes and the subject's head was made by using

conductive gel. Preparation time was about 20-30

minutes.

The ASA software incorporates all recording

functions for optimal data acquisition. The interface

is intuitive and allows displaying online EEG, spectra

and frequency maps, averaging, triggers, and

response statistics. In addition to the ASA software,

EEG Lab in MATLAB was used for the analysis of

the EEG data.

Visual Database: A public-domain library with

emotion-annotated images (IAPS) was used in this

study (Lang, Bradley, and Cuthbert, 2005; Mikels,

Fredrickson, Larkin, Lindberg, Maglio, and Reuter-

Lorenz, 2005). Each emotion was evoked by

exposure to a set of static images based on a

dimensional model of emotion. Neutral pictures

included sceneries and household objects; positive

pictures included food items, children and happy

families; negative pictures included snakes, mutilated

bodies and scenes of attack and threat. Every picture

was presented on a 17-in computer monitor, situated

approximately 0.5 m from the participant, leading to

an image presentation with a visible angle of 15

degrees horizontally and 11 degrees vertically. Each

picture was presented for 3 s. The IAPS catalog

numbers for pictures used in this study are as follows:

neutral: 5720, 5725, 7035, 7042, 7057, 7547, 7490,

7545, 7041, 7700, 7500, 7180, 7175, 7090, 7080,

7050, 7030, 7020, 7010, 5500; negative: 2205, 9050,

9075, 9220, 2688, 1300, 1120, 3100, 1070, 3000,

1050, 6370, 1090, 3350, 1930, 9040, 9490, 6260,

6510, 2345.1; positive: 2345, 2332, 2530, 2216,

2091, 1600, 2050, 2070, 7405, 2311, 2340, 2341,

7200, 2347, 2208, 2224, 2306, 2374, 7350, 7330.

Step 3: Artifact Filtering

This step removed the eye movement/blinking (most

dominant below 4Hz) from the recorded EEG data. A

low-pass filter with a cut-off frequency of 50Hz was

used.

Step 4: Five EEG frequency ranges

Band-pass filtering was used to extract five EEG

bands for each lobe: Delta (0-3.9Hz), Theta (4-

7.9Hz), Alpha (8-12.9Hz), Beta (13-30 Hz), and

Gamma (31-50Hz). Brain waves of different

frequency range are generally associated with

different brain activities. The delta band frequency, or

delta activity, is highly dominant in deep sleep of

adults and infants. Harmony showed that during

concentration on a task, the delta activity increased in

mental calculation, and also during semantic tasks

(Harmony, 2013). The theta activity is seen in sleep,

drowsiness of adults, in infants and children. The high

theta activity in adults who were awake suggested

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that there were some abnormal pathological

conditions (Subha, 2010). The alpha activity is

present in learning, light trance and relaxed adults.

The beta activity is present in alertness states and

during learning. Gamma activity refers to the state of

multi-tasking, namely simultaneous processing. In

this study we analyzed the influence of three

emotional states on memory by analyzing the five

EEG frequency bands.

Step 5: Analysis

The extracted five EEG bands were analyzed for each

task and corroborated with the three emotional states.

Data were evaluated along with the results from

memory tests. Average band energy was used as a

way to compare neuronal activities in mental tasks

and three emotional arousals. The average signal

energy was calculated as

=





()





(1)

where x(n) is the signal and N is the signal size,

0<N<∞.

4 DATA ANALYSIS

In order to evaluate the level of impact perceived by

each participant while viewing emotion-annotated

images, a self-assessment sheet was provided to

participants after completion of the experiment.

Participants were asked to score the emotion-

annotated images for emotional arousal as not

effective, and effective. 20% of the participants found

the neutral images not effective and 80% found them

effective. All participants found the negative images

(thought of as inducing sadness) effective.

Interestingly, only 60% of the participants found the

positive images (thought of as inducing happiness)

effective while the remainder thought positive images

did not arouse positive emotions. We concluded that

evoking positive emotions was more difficult than

evoking negative emotions in study participants.

Nonetheless, these percentages of perceived

effectiveness of stimuli are sufficient to make our

research findings meaningful.

The capacity to memorize of study participants

was evaluated by assessing the percentage of words

or numbers recalled from a list. More numbers or

words recalled from a list was used as an indicator of

increased short-term memory retention, which can be

conducive to learning. The memory retention was

then corroborated with exposure to various emotional

stimuli from the visual library.

Data were analyzed as frequency of recalled

words, event-related words, and numbers dependent

upon participants’ exposure to various emotional

stimuli that were categorized as neutral, negative (i.e.,

sadness), and positive (i.e., happiness). Expected

frequencies were considered equal for each evoked

emotion and this null hypothesis was tested at 0.05

significance level.

A chi-square test of independence was calculated

comparing the frequency of recalled event-related

words and evoked emotions. A significant interaction

was found (χ

(4) = 10.485, p < 0.05). Participants

exposed to negative emotions tended to recall more

event-related words (56.25%) than participants

exposed to positive emotions (52.94%) or neutral

emotions (50%). We can conclude that negative

emotions can have a strong effect on memory

retention and in consequence, when exposed to words

related to such emotions, individuals recall these

words better.

A chi-square test of independence was also

calculated comparing the frequency of recalled

numbers and evoked emotions. A significant

interaction was found (χ

(7) = 22.030, p < 0.05).

Participants exposed to positive emotions tended to

recall more numbers (56%) than participants exposed

to negative emotions (42%) or neutral emotions (28

%). We can conclude that participants’ capacity to

memorize numbers was related to emotion: positive

and negative emotions increased memory retention

when compared to neutral emotion. Nonetheless,

exposure to positive emotions increased individuals’

capacity to memorize numbers.

A chi-square test of independence was also

calculated comparing the frequency of recalled words

(not related to any emotion) and evoked emotions. No

significant interaction was found at the 0.05 level.

These preliminary findings suggest that any emotion

(positive or negative) can have a considerable effect

on the capacity to memorize emotion-related words.

This finding comes in contradiction to results

reported by Valiente et al. (2012), who found that the

highest ability to memorize was associated with

neutral emotions, such as being in a calm state.

Concerning the short-term memorization of numbers,

in average, participants recollected 28% numbers

after exposure to neutral stimuli, 42% numbers after

The Inﬂuence of Emotional States on Short-term Memory Retention by using Electroencephalography (EEG) Measurements: A Case Study

209

exposure to negative emotional stimuli, and 56%

numbers after exposure to positive emotional stimuli.

This can indicate that, when memorizing numbers,

emotional states can lead to increased memory

retention, and in particular positive emotional stimuli

increase short-term memory retention.

We therefore conclude that the memorization of

words that are related in some way to the visual

stimulus or the emotion induced by it, leads to

increased memory retention. In other words,

connecting the word to be memorized with the visual

stimulus or the emotion induced by it increased the

memorization capacities of study participants.

Context is conducive to learning. Moreover, when

memory retention, as percentage of words

recollected, was compared according to the type of

emotional stimulus, it was found that exposure to

negative emotional stimuli led to the highest memory

retention capacity. Memory retention after exposure

to neutral and positive stimuli were lower that after

exposure to negative stimuli, with the memory

retention being lowest after exposure to neutral

stimuli. This indicates that, for words that are related

to the type of stimulus, memory retention increases.

Emotional state can play a role on how context is

conducive to learning. We were surprised by our

findings that suggested an increased memory

retention of event-related words after exposure to

negative emotional stimuli. We suppose that negative

emotions may have a more powerful impact on the

short-term memory retention, especially when

relating the word to the type of negative emotional

stimulus.

Analysis of ERPs in response to all five tasks was

performed on the basis that ERPs can serve as

indicators of brain changes that take place during

engagement in learning, such as the memorization of

numbers and/or words (Kelly and Garavan, 2005).

Figure 3 shows the average energy distribution for

each EEG band (alpha, beta, delta, gamma, and theta)

after each of the five tasks (relaxation, memorization

of words, memorization of numbers, exposure to

visual emotional stimuli, and recall of words and

numbers).

Alpha, Beta, Delta, and Gamma activities are related

to learning, alertness, mental calculation, and

simultaneous processing, respectively (Harmony,

2013; Subha, 2010). The highest average energy for

all tasks was observed in the Delta band. When the

participants were recalling words and numbers,

Alpha, Beta, and Gamma activities appeared slightly

higher after the positive emotion arousal compared to

Table 1: Recall percentages according to exposure to

various emotional events.

Event

Recall

Neutral

emotion

Negative

emotion

Positive

emotion

Nonevent-

related words

40% 32% 34%

Event-related

words

50% 56.25% 52.94%

Numbers 28% 42% 56%

neutral and negative emotion arousals. We also

observed that activities in all EEG bands were the

highest while the participants were memorizing

words. This is shown in Figure 4.

Our interpretation of this finding takes into

account the participants’ strong background in

mathematics. As engineering graduate students,

participants have had extensive exposure to tasks

involving numbers and master mathematical concepts

on a daily basis. As such, we conclude that dealing

with numbers appears as an easy task to them and in

consequence, such task does not require increased

brain activity, as assessed by EEG measurements.

5 CONCLUSIONS AND FUTURE

WORK

To date, no other studies have been reported to use

EEG measurements in the evaluation of the influence

of emotional states on short-term memory retention.

Participants were engaged in five mental tasks,

namely relaxation, memorization of a list of 10

words, memorization of a list of 10 numbers,

exposure to neutral/negative/positive visual stimuli,

and recall of words/numbers from the lists. ERPs

were recorded with the ANT Neuro system with

625Hz sampling frequency, and processed by using

the ASA software and EEG Lab in MATLAB.

Preliminary results are presented herewith. Based on

participants’ perception of the test, evoking positive

emotions was more difficult than evoking negative

emotions. Moreover, study participants recalled more

event-related words after exposure to negative

emotional stimuli. Participants may find more

emotional relevance in words. Memorizing and

recalling numbers may be processes more sensitive to

the influence of emotional states. That said,

percentage of recalled numbers was higher after

exposure to positive emotional stimuli. So, there is an

evidence that positive emotions can lead to increased

memory retention.

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210

(a) (b)

(e)

Figure 3: Average energy (E) distribution for events and EEG bands. (a) Relaxation; (b) Memorization of words; (c)

Memorization of numbers; (d) Emotional arousal; (e) Recall of words and numbers. Blue bars indicate average energy levels

of corresponding ERPs after exposure to neutral visual stimuli; green bars indicate average energy levels of ERPs after

exposure to negative visual stimuli; yellow bars indicate average energy levels of ERPs after exposure to positive visual

stimuli.

Another important finding is that participants

recalled more event-related words from the list when

exposed to emotional stimuli related to the words. For

example, words such as ‘caring’ tended to be recalled

easier when exposed to positive emotional stimuli,

such as images of families. We can conclude that

context is conducive to learning.

Although we collected valuable evidence of the

influence of emotional states on short-term memory

retention, our interpretation of the data is limited due

to the small number of participants included, their

background (graduate students in engineering), and

gender (six male participants). In the future, we plan

to extend the study to include more participants, in

particular females and individuals from different

academic backgrounds, such as humanities and social

sciences.

The Inﬂuence of Emotional States on Short-term Memory Retention by using Electroencephalography (EEG) Measurements: A Case Study

211

Figure 4: Average energy for EEG bands recorded during

the memorization of words and numbers. The blue line

corresponds to the memorization of words, and the red line

corresponds to the memorization of numbers.

ACKNOWLEDGEMENTS

This research was supported by University of

Bridgeport Seed Money grant (UBSMG2016).

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